Optimisation Méthodes Numériques, 1976. ,
Approximation et optimisation, 1972. ,
Analyse numérique des algorithmes de l'optimisation DC. Approches locale et globale. Codes et simulations numériques en grande dimension ,
ContributionàContributionà l'optimisation non convexe et l'optimisation globale: Théorie, Algorithmes et Applications, HabilitationàHabilitationà Diriger des Recherches, 1997. ,
Solving a class of linearly constrained indefinite quadratic problems by DC algorithms, Journal of Global Optimization, vol.11, issue.3, pp.253-285, 1997. ,
DOI : 10.1023/A:1008288411710
URL : https://hal.archives-ouvertes.fr/hal-01636781
Combination between Local and Global Methods for Solving an Optimization Problem over the Efficient Set, European Journal of Operational Research, vol.142, pp.257-270, 2002. ,
URL : https://hal.archives-ouvertes.fr/hal-01636770
DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Ann. Oper. Res, pp.23-46, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01636759
Elements homoduaux relatifsàrelatifsà un couple de normes (?, ?) Applications au calcul de S ?? (A), 1975. ,
Calcul du maximum d'une forme quadratique définie positive sur la boule unité de la norme du max, 1976. ,
Algorithms for solving a class of non convex optimization problems. Methods of subgradients. Fermat days 85, Mathematics for Optimization, 1986. ,
Duality in DC (difference of convex functions) optimization. Subgradient methods, Trends in Mathematical Optimization, International Series of Numer Math, pp.277-293, 1988. ,
Convex analysis approach to d.c. programming: Theory, Algorithm and Applications, Acta Mathematica Vietnamica, vol.22, pp.289-355, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01664714
DC optimization algorithms for solving the trust region sub-problem, SIAM J. Optim, vol.8, pp.476-505, 1998. ,
Recent advances in DC programming and DCA, Transactions on Computational Collective Intelligence, vol.8342, pp.1-37, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01664024
Global Optimization: Deterministic Approaches, 1996. ,
Convex Analysis, 1970. ,
DOI : 10.1515/9781400873173
DC Optimisation : Theory, Methods and Algorithms, Handbook of Global Optimisation. Horst and Pardalos eds, pp.149-216, 1995. ,
Duality in nonconvex optimization, Journal of Mathematical Analysis and Applications, vol.66, issue.2, pp.399-415, 1978. ,
DOI : 10.1016/0022-247X(78)90243-3
Generalized differentiability, duality and optimization for problem dealing with differences of convex functions, Lecture Notes in Economics and Mathematical Systems, vol.256, pp.260-277, 1985. ,
Conditions nécessaires et suffisantes d'optimalité globale en optimisation de différences de deux fonctions convexes, pp.459-462, 1989. ,
Modularity-maximizing graph communities via mathematical programming, The European Physical Journal B, vol.66, issue.3 ,
DOI : 10.1140/epjb/e2008-00425-1
Column generation algorithms for exact modularity maximization in networks, Physical Review E, vol.1, issue.4, p.46112, 2010. ,
DOI : 10.1016/j.physrep.2009.11.002
URL : https://hal.archives-ouvertes.fr/hal-00934661
Analysis of the structure of complex networks at different resolution levels, New Journal of Physics, vol.10, issue.5, p.39053039, 2008. ,
DOI : 10.1088/1367-2630/10/5/053039
Detecting network communities by propagating labels under constraints, Physical Review E, vol.33, issue.2, p.26129, 2009. ,
DOI : 10.1073/pnas.0605965104
URL : http://arxiv.org/pdf/0903.3138
Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, pp.1742-5468, 2008. ,
DOI : 10.1088/1742-5468/2008/10/P10008
URL : https://hal.archives-ouvertes.fr/hal-01146070
Detecting complex network modularity by dynamical clustering, Physical Review E, vol.33, issue.4, 2007. ,
DOI : 10.1103/PhysRevE.70.056104
Feature selection via concave minimization and support vector machines, Machine Learning Proceedings of the Fifteenth International Conferences (ICML'98), pp.82-90, 1998. ,
On Modularity Clustering, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.2, pp.172-188, 2008. ,
DOI : 10.1109/TKDE.2007.190689
Locally optimal heuristic for modularity maximization of networks, Physical Review E, vol.33, issue.5, p.56105, 2011. ,
DOI : 10.1103/PhysRevE.70.056122
URL : https://hal.archives-ouvertes.fr/hal-00934660
Finding community structure in very large networks, Physical Review E, vol.23, issue.6, 2004. ,
DOI : 10.1140/epjb/e2004-00125-x
Trading convexity for scalability, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006. ,
DOI : 10.1145/1143844.1143870
Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Serie B, vol.39, issue.1, pp.1-38, 1977. ,
Community Detection in Scale-Free Networks: Approximation Algorithms for Maximizing Modularity, Selected Areas in Communications, IEEE Journal on, vol.31, issue.6, p.997, 1006. ,
A Scalable Multilevel Algorithm for Graph Clustering and Community Structure Detection, Lecture Notes in Computer Science, vol.4936, 2008. ,
DOI : 10.1007/978-3-540-78808-9_11
Community detection in complex networks using extremal opti-mization, Phys. Rev. E, vol.72104, issue.027, 2005. ,
A Convex Formulation of Modularity Maximization for Community Detection, proceedings of the 22nd International Joint Conference on Artificial Intelligence, pp.2218-2225, 2011. ,
Line graphs, link partitions, and overlapping communities, Physical Review E, vol.8, issue.1, 2009. ,
DOI : 10.1088/1367-2630/10/5/053039
Resolution limit in community detection, proc. Natl.Acad.Sci.USA, 2007. ,
DOI : 10.1126/science.298.5594.824
Community detection in graphs, Physics Reports, vol.486, issue.3-5, pp.75-174, 2010. ,
DOI : 10.1016/j.physrep.2009.11.002
Functional cartography of complex metabolic networks, Nature, vol.411, issue.Suppl., p.895, 2005. ,
DOI : 10.1038/35075138
On Clustering Using Random Walks, 21st conf. on Foundations of Software Technology and Theoretical Computer Science, pp.18-41, 2001. ,
DOI : 10.1007/3-540-45294-X_3
Modularity functions maximization with nonnegative relaxation facilitates community detection in networks, Physica A: Statistical Mechanics and its Applications, pp.854-865, 2012. ,
Mod-CSA: Modularity optimization by conformational space annealing, eprint arXiv, pp.1202-5398, 2012. ,
Enhanced modularity-based community detection by random walk network preprocessing, Physical Review E, vol.33, issue.6, 2010. ,
DOI : 10.1103/PhysRevE.80.016118
Laplacian dynamics and multiscale mod-ular structure in networks, 2008. ,
The DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, pp.23-46, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01636759
A new efficient algorithm based on DC programming and DCA for Clustering, Journal of Global Optimization, vol.37, pp.593-608, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-01636755
Optimization based DC programming and DCA for Hierarchical Clustering, European Journal of Operational Research, vol.183, pp.1067-1085, 2006. ,
Fuzzy clustering based on nonconvex optimisation approaches using difference of convex (DC) functions algorithms, Advances in Data Analysis and Classification, vol.15, issue.3, pp.1-20, 2007. ,
DOI : 10.1515/9781400873173
URL : https://hal.archives-ouvertes.fr/hal-01636753
A DC programming approach for feature selection in support vector machines learning, Advances in Data Analysis and Classification, vol.3, issue.1???3, pp.259-278, 2008. ,
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-01636751
Gene selection for cancer classification using DCA, Adv. Dat. Min. Appl. LNCS, vol.5139, pp.62-72, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-01664633
-Learning, Journal of the American Statistical Association, vol.101, issue.474, pp.500-509, 2006. ,
DOI : 10.1198/016214505000000781
Deterministic modularity optimization, The European Physical Journal B, vol.435, issue.1, pp.83-88, 2007. ,
DOI : 10.1140/epjb/e2007-00313-2
Quantitative function for community detection, Physical Review E, vol.33, issue.3, pp.77-036109, 2008. ,
DOI : 10.1140/epjb/e2007-00146-y
Community detection by modularity maximization using GRASP with path relinking, Computers & Operations Research, 2013. ,
Identifying communities within energy landscapes, Physical Review E, vol.225, issue.4, 2005. ,
DOI : 10.1063/1.1436470
Detection of community structures in networks via global optimization, Physica A: Statistical Mechanics and its Applications, vol.358, issue.2-4, pp.593-604, 2005. ,
DOI : 10.1016/j.physa.2005.04.022
Graph Spectra and the Detectability of Community Structure in Networks, Physical Review Letters, vol.108, issue.18, p.188701, 2012. ,
DOI : 10.1103/PhysRevE.78.046110
Fast algorithm for detecting community structure in networks, Physical Review E, vol.33, issue.6, 2004. ,
DOI : 10.1098/rsbl.2003.0057
Finding community structure in networks using the eigenvectors of matrices, Physical Review E, vol.49, issue.3, 2006. ,
DOI : 10.1103/PhysRevE.72.046105
Modularity and community structure in networks, Proc. Natl. Acad. Sci. USA 103, p.85778582, 2006. ,
DOI : 10.1073/pnas.021544898
Networks: An Introduction, 2010. ,
DOI : 10.1093/acprof:oso/9780199206650.001.0001
Finding and evaluating community structure in networks, Physical Review E, vol.65, issue.2, 2004. ,
DOI : 10.1103/PhysRevE.68.065103
SVM-Based Feature Selection by Direct Objective Minimisation, Proc. of 26th DAGM Symposium, pp.212-219, 2004. ,
DOI : 10.1007/978-3-540-28649-3_26
Learning with sparsity by Difference of Convex functions Algorithm, Press 27, p.14, 2011. ,
Convex analysis approach to d.c programming: Theory, algorithms and applications, Acta Mathematica Vietnamica, pp.289-355, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01664714
DC optimization algorithms for solving the trust region subproblem, SIAM J. Optimization pp, pp.476-505, 1998. ,
Computing communities in large networks using random walks, J. of Graph Alg. and App, vol.10, pp.284-293, 2004. ,
Near linear time algorithm to detect community structures in large-scale networks, Physical Review E, vol.33, issue.3, p.36106, 2007. ,
DOI : 10.1140/epjb/e2004-00130-1
Spectral tripartitioning of networks, Physical Review E, vol.49, issue.3, p.36111, 2009. ,
DOI : 10.1038/nature06830
Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement, Physical Review E, vol.36, issue.4, 2008. ,
DOI : 10.1038/43601
On ??-Learning, Journal of the American Statistical Association, vol.98, issue.463, pp.724-734, 2003. ,
DOI : 10.1198/016214503000000639
The Convex Concave Procedure (CCCP) Advances, in Neural Information Processing System, 2002. ,
Prior Learning and Convex-Concave Regularization of Binary Tomography, Electronic Notes in Discrete Mathematics, vol.20, pp.313-327, 2005. ,
DOI : 10.1016/j.endm.2005.05.071
Improved community structure detection using a modified fine-tuning strategy, EPL (Europhysics Letters), vol.86, issue.2, p.28004, 2009. ,
DOI : 10.1209/0295-5075/86/28004
Community detection in complex networks using genetic algorithms, 2007. ,
Graph clustering with local search optimization: The resolution bias of the objective function matters most, Phys. Rev. E, vol.87, p.12812, 2013. ,
Graph clustering by flow simulation, 2000. ,
Finding Community Structure in Mega-scale Social Networks, arXiv e-print cs, p.702048, 2007. ,
A Spectral Clustering Approach To Finding Communities in Graphs, Proceedings of the 5th SIAM International Conference on Data Mining, pp.274-285, 2005. ,
DOI : 10.1137/1.9781611972757.25
Finding community structures in complex networks using mixed integer optimisation, The European Physical Journal B, vol.579, issue.2, pp.231-239, 2007. ,
DOI : 10.1086/jar.33.4.3629752
Networks: An Introduction, 2010. ,
DOI : 10.1093/acprof:oso/9780199206650.001.0001
Community structure in social and biological networks, Proceedings of the National Academy of Science, pp.7821-7826, 2002. ,
DOI : 10.1086/285382
Comparing community structure identification, Journal of Statistical Mechanics: Theory and Experiment, vol.2005, issue.09, 2005. ,
DOI : 10.1088/1742-5468/2005/09/P09008
Finding and evaluating community structure in networks, Physical Review E, vol.65, issue.2, p.26113, 2004. ,
DOI : 10.1103/PhysRevE.68.065103
Resolution limit in community detection, proc. Natl.Acad.Sci.USA, p.36, 2007. ,
DOI : 10.1126/science.298.5594.824
On Finding Graph Clusterings with Maximum Modularity, proc 33rd intl workshop graphtheoretic concepts in computer science (WG07), 2007. ,
DOI : 10.1007/978-3-540-74839-7_12
Fast unfolding of communities in large networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2008, issue.10, 2008. ,
DOI : 10.1088/1742-5468/2008/10/P10008
URL : https://hal.archives-ouvertes.fr/hal-01146070
Computing communities in large networks using random walks, J. of Graph Alg. and App. bf, vol.10, pp.284-293, 2004. ,
Modularity and community structure in networks, Proc. Natl. Acad. Sci. USA, p.85778582, 2006. ,
DOI : 10.1073/pnas.021544898
Community detection in complex networks using extremal optimization, Physical Review E, vol.2004, issue.2, p.27104, 2005. ,
DOI : 10.1038/nature03288
Finding community structure in networks using the eigenvectors of matrices, Physical Review E, vol.49, issue.3, p.36104, 2006. ,
DOI : 10.1103/PhysRevE.72.046105
Efficient modularity optimization by multistep greedy algorithm and vertex mover refinement, Physical Review E, vol.36, issue.4, p.46112, 2008. ,
DOI : 10.1038/43601
Cluster analysis for applications, 1973. ,
Fast algorithm for detecting community structure in networks, Physical Review E, vol.33, issue.6, p.66133, 2004. ,
DOI : 10.1098/rsbl.2003.0057
Finding community structure in very large networks, Physical Review E, vol.23, issue.6, p.66111, 2004. ,
DOI : 10.1140/epjb/e2004-00125-x
Modern hierarchical, agglomerative clustering algorithms, Lecture Notes in Computer Science, p.391829, 1973. ,
Multi-level Algorithms for Modularity Clustering, Proceedings of the 8th International Symposium on Experimental Algorithms, SEA '09, pp.257-268, 2009. ,
DOI : 10.1038/43601
Finding community structure in mega-scale social networks. eprint arXiv:cs/0702048, 2007. ,
The effect of size heterogeneity on community identification in complex networks, Journal of Statistical Mechanics: Theory and Experiment, vol.2006, issue.11, 2006. ,
DOI : 10.1088/1742-5468/2006/11/P11010
Efficient algorithms for agglomerative hierarchical clustering methods, Journal of Classification, vol.25, issue.1, pp.7-24, 1984. ,
DOI : 10.1093/comjnl/26.4.354
On community detection in very large networks, Complex Networks, pp.208-216, 2011. ,
Deterministic modularity optimization, The European Physical Journal B, vol.435, issue.1, pp.83-88, 2007. ,
DOI : 10.1140/epjb/e2007-00313-2
Image Compression Using Self-Organizing Maps. Systems Analysis Modelling Simulation, pp.1529-1543, 2003. ,
Clustering Hierarchical Data Using Self-Organizing Map: A Graph-Theoretical Approach Advances in Self-Organizing Maps, Lecture Notes in Computer Science, pp.19-27, 2009. ,
Topology-oriented self-organizing maps: a survey. Pattern Analysis and Applications, pp.1-26, 2014. ,
Self-Organizing Feature Maps for Modeling and Control of Robotic Manipulators, Journal of Intelligent and Robotic Systems, vol.36, issue.4, pp.407-450, 2003. ,
DOI : 10.1023/A:1023641801514
Feature selection via concave minimization and support vector machines, Machine Learning Proceedings of the Fifteenth International Conferences (ICML'98), pp.82-90, 1998. ,
Phase transitions in stochastic self-organizing maps, Physical Review E, vol.56, pp.3876-3890, 1997. ,
Classification Of Documents Using Kohonens Self-Organizing Map, International Journal of Computer Theory and Engineering, vol.1, issue.5, pp.610-613, 2009. ,
Skin detection using a modified Self-Organizing Mixture Network, Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops, pp.22-26, 2013. ,
Batch self-organizing maps based on city-block distances for interval variables, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00706519
Maximum likelihood from incomplete data via the EM algorithm, J. Roy. Stat. Soc. B, 1977. ,
Music recommendation and query-by-content using Self-Organizing Maps, 2009 International Joint Conference on Neural Networks, pp.705-710, 2009. ,
DOI : 10.1109/IJCNN.2009.5178975
Self-Organizing Map and Tree Topology for Graph Summarization, Artificial Neural Networks and Machine Learning ICANN 2012, pp.363-370, 2012. ,
DOI : 10.1007/978-3-642-33266-1_45
An Algorithm of SOM using Simulated Annealing in the Batch Update Phase for Sequence Analysis, International Workshop on Self-Organizing Maps (WSOM), pp.171-178, 2005. ,
Improved SOM Learning Using Simulated Annealing, Lecture Notes in Computer Science, vol.4668, pp.279-288, 2007. ,
DOI : 10.1007/978-3-540-74690-4_29
Advantages and drawbacks of the Batch Kohonen algorithm, The European Symposium on Artificial Neural Networks conference -ESANN, pp.223-230, 2002. ,
Topographic Measure Based on External Criteria for Self-Organizing Map Advances in Self-Organizing Maps, Lecture Notes in Computer Science, pp.131-140, 2011. ,
Application of ART2 Networks and Self-Organizing Maps to Collaborative Filtering, Hypermedia: Openness, Structural Awareness, and Adaptivity, pp.296-309, 2002. ,
DOI : 10.1007/3-540-45844-1_27
Self-organizing maps: Generalizations and new optimization techniques, Neurocomputing, vol.21, issue.1-3, pp.173-190, 1998. ,
DOI : 10.1016/S0925-2312(98)00035-6
Self-organizing map for clustering in the graph domain, Pattern Recognition Letters, vol.23, issue.4, pp.405-417, 2002. ,
DOI : 10.1016/S0167-8655(01)00173-8
BAYESIAN SELF-ORGANIZING MAP FOR DATA CLASSIFICATION AND CLUSTERING, International Journal of Wavelets, Multiresolution and Information Processing, vol.11, issue.05, p.12, 2013. ,
DOI : 10.1016/j.patrec.2010.08.007
Graph self-organizing maps for cyclic and unbounded graphs, Neurocomputing, vol.72, issue.7-9, pp.1419-1430, 2009. ,
DOI : 10.1016/j.neucom.2008.12.021
Hierarchical self-organizing maps for clustering spatiotemporal data, International Journal of Geographical Information Science, vol.1, issue.5, pp.2026-2042, 2013. ,
DOI : 10.1016/j.ecolmodel.2008.12.016
Using Self-Organizing Maps for object classification in Epo image analysis, MEASUREMENT SCIENCE REVIEW, vol.5, issue.2, pp.11-16, 2005. ,
Energy functions for self organizingmaps, pp.303-316, 1999. ,
Self-Organization Maps, vector quantization, and mixture modeling, IEEE transactions on neural networks, vol.12, issue.6, 2001. ,
Convex Analysis and Minimization Algorithms, SpringerVerlag berlin Heidelberg, 1993. ,
A hybrid model of self organizing maps and least square support vector machine for river flow forecasting, Hydrology and Earth System Sciences, vol.16, issue.11, pp.4417-4433, 2012. ,
DOI : 10.5194/hess-16-4417-2012
Comparing self-organizing maps, Lecture Notes in Computer Science, vol.1112, pp.809-814, 1996. ,
DOI : 10.1007/3-540-61510-5_136
Topology Preservation in Fuzzy Self-Organizing Maps, Advance Trends in Soft Computing, Studies in Fuzziness and Soft Computing, pp.105-114, 2014. ,
DOI : 10.1007/978-3-319-03674-8_10
Topology preservation in self-organizing maps, Proceedings of International Conference on Neural Networks (ICNN'96), pp.294-299, 1996. ,
DOI : 10.1109/ICNN.1996.548907
Analysis of a simple self-organizing process, Biological Cybernetics, vol.43, issue.2, pp.135-140, 1982. ,
DOI : 10.1007/BF00317973
Self-Organization Maps, 1997. ,
Leveraging the margin more carefully, Twenty-first international conference on Machine learning , ICML '04, 2004. ,
DOI : 10.1145/1015330.1015344
DC Programming and DCA ,
URL : https://hal.archives-ouvertes.fr/hal-01664024
Solving a class of linearly constrained indefinite quadratic problems by D.c. algorithms, Journal of Global Optimization, vol.11, issue.3, pp.253-285, 1997. ,
DOI : 10.1023/A:1008288411710
URL : https://hal.archives-ouvertes.fr/hal-01636781
DC (difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Ann. Oper. Res, vol.133, pp.23-46, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01636759
Fuzzy clustering based on nonconvex optimization approaches using difference of convex (DC) functions algorithms, Journal of Advances in Data Analysis and Classification, vol.2, pp.1-20, 2007. ,
A DC programming approach for feature selection in support vector machines learning, Advances in Data Analysis and Classification, vol.3, issue.1???3, pp.259-278, 2008. ,
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-01636751
Binary classification via spherical separator by DC programming and DCA, Journal of Global Optimization, pp.1-1510, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-01636671
Block Clustering based on DC programming and DCA. NECO -Neural Computation, pp.2776-2807, 2013. ,
DC Programming and DCA for Diversity Data Mining ,
New and efficient DCA based algorithms for Minimum Sumof-Squares Clustering, press in Patter Recognition, 2013. ,
A Hybrid Recommender System Combining Collaborative Filtering with Neural Network, Lecture Notes in Computer Science, vol.2347, pp.531-534, 2002. ,
DOI : 10.1007/3-540-47952-X_77
Objectionable Image Detection by ASSOM Competition, Lecture Notes in Computer Science, vol.4071, pp.201-210, 2006. ,
DOI : 10.1007/11788034_21
URL : https://hal.archives-ouvertes.fr/hal-01224269
Multicategory ??-Learning and Support Vector Machine: Computational Tools, Journal of Computational and Graphical Statistics, vol.14, issue.1, pp.219-236, 2005. ,
DOI : 10.1198/106186005X37238
-Learning, Journal of the American Statistical Association, vol.101, issue.474, pp.500-509, 2006. ,
DOI : 10.1198/016214505000000781
Multiple Kernel Self-Organizing Maps. ESANN 2013 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), pp.83-88, 2013. ,
Self-organizing Maps. Handbook of Natural Computing, pp.585-622, 2012. ,
Graph Mining Based SOM: A Tool to Analyze Economic Stability, Applications of Self- Organizing Maps, Magnus Johnsson edit, pp.1-25, 2012. ,
Fast Growing Self Organizing Map for Text Clustering, Neural Information Processing, pp.406-415, 2011. ,
DOI : 10.1109/TNN.2002.804221
Batch-Learning Self-Organizing Map with Weighted Connections avoiding false-neighbor effects, The 2010 International Joint Conference on Neural Networks (IJCNN), pp.1-6, 2010. ,
DOI : 10.1109/IJCNN.2010.5596524
Self-Organizing Map Formation with a Selectively Refractory Neighborhood, Neural Processing Letters, vol.90, issue.1, pp.1-24, 2014. ,
DOI : 10.1016/j.biosystems.2006.07.004
Clustering of Pressure Fluctuation Data Using Self-Organizing Map, Communications in Computer and Information Science, vol.48, pp.45-54, 2009. ,
DOI : 10.1016/j.neucom.2008.10.028
Cierco-Ayrolles C.: Multiple Kernel Self-Organizing Maps. Hal- 00817920, 2013. ,
Layered Self-Organizing Map for Image Classification in Unrestricted Domains, Image Analysis and Processing -ICIAP 2013, pp.310-319, 2013. ,
DOI : 10.1007/978-3-642-41181-6_32
Image Segmentation By Self Organizing Map With Mahalanobis Distance, International Journal of Emerging Technology and Advanced Engineering, vol.3, issue.2, pp.288-291, 2013. ,
Convex analysis approach to d.c. programming: Theory, Algorithms and Applications (dedicated to Professor Hoang Tuy on the occasion of his 70th birthday, Acta Mathematica Vietnamica, pp.289-355, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01664714
DC optimization algorithms for solving the trust region sub-problem, SIAM J. Optim, vol.8, pp.476-505, 1998. ,
Survey and Comparison of Quality Measures for Self-Organizing Maps. In: WDA 2004. Fifth Workshop on Data Analysis, pp.67-82, 2004. ,
The Use of Self Organizing Map Method and Feature Selection in Image Database Classification System, International Journal of Computer Science Issues, vol.9, issue.3 2, pp.377-381, 2012. ,
The collaborative filtering recommendation based on SOM clusterindexing CBR. Expert Systems with Applications, pp.413-423, 2003. ,
A New Dynamic Self-Organizing Method for Mobile Robot Environment Mapping, Journal of Intelligent Learning Systems and Applications, vol.03, issue.04, pp.249-256, 2011. ,
DOI : 10.4236/jilsa.2011.34028
On Document Classification with Self-Organising Maps, Lecture Notes in Computer Science, vol.10, issue.1, pp.260-269, 2011. ,
DOI : 10.1007/s10791-006-9012-6
Fuzzy Clustering of the Self-Organizing Map: Some Applications on Financial Time Series, Lecture Notes in Computer Science, vol.13, issue.8, pp.40-50, 2011. ,
DOI : 10.1109/34.85677
On ??-Learning, Journal of the American Statistical Association, vol.98, issue.463, pp.724-734, 2003. ,
DOI : 10.1198/016214503000000639
Growing Self-Organizing Map for Online Continuous Clustering, Foundations of Computational Intelligence Studies in Computational Intelligence, vol.4, issue.204, pp.49-83, 2009. ,
DOI : 10.1007/978-3-642-01088-0_3
Influence of Learning Rates and Neighboring Functions on Self-Organizing Maps, Lecture Notes in Computer Science, vol.36, issue.1, pp.141-150, 2011. ,
DOI : 10.1007/978-3-642-56927-2
Self Organizing Maps for Visualization of Categories, Neural Information Processing, pp.160-167, 2012. ,
DOI : 10.1007/978-3-642-34475-6_20
Self-organising Map Techniques for Graph Data Applications to Clustering of XML Documents, Lecture Notes in Computer Science, vol.4093, pp.19-30, 2006. ,
DOI : 10.1007/11811305_2
Combining the Self-Organizing Map and K-Means Clustering for On-Line Classification of Sensor Data, Artificial Neural Networks ICANN 2001, pp.464-469, 2001. ,
DOI : 10.1007/3-540-44668-0_65
A Self-Organizing Map Based Knowledge Discovery for Music Recommendation Systems, Lecture Notes in Computer Science, vol.3310, pp.119-129, 2004. ,
DOI : 10.1007/978-3-540-31807-1_9
Clustering of the self-organizing map, IEEE Transactions on Neural Networks, vol.11, issue.3, pp.586-600, 2000. ,
DOI : 10.1109/72.846731
Clustering a medieval social network by SOM using a kernel based distance measure, proceedings -European Symposium on Artificial Neural Networks Bruges (Belgium), dside publi, 2007. ,
URL : https://hal.archives-ouvertes.fr/hal-00145117
On transductive support vector machines, Proceeding of the International Conference on Machine Learning ICML, 2007. ,
DOI : 10.1090/conm/443/08551
Self-Organising Maps for Image Segmentation Advances in Data Analysis, Data Handling and Business Intelligence Studies in Classification, Data Analysis, and Knowledge Organization, pp.373-383, 2010. ,
The Self-Organizing Maps: Background, Theories, Extensions and Applications, Studies in Computational Intelligence (SCI), vol.115, pp.715-762, 2008. ,
DOI : 10.1007/978-3-540-78293-3_17
The Convex Concave Procedure (CCCP) Advances in Neural Information Processing System 14, 2002. ,
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems, Theoretical Computer Science, vol.209, issue.1-2, pp.237-260, 1998. ,
DOI : 10.1016/S0304-3975(97)00115-1
Semi-Supervised Learning on Riemannian Manifolds, Machine Learning, pp.209-239, 2004. ,
DOI : 10.1023/B:MACH.0000033120.25363.1e
URL : https://link.springer.com/content/pdf/10.1023%2FB%3AMACH.0000033120.25363.1e.pdf
Semi-supervised support vector machines, Proceedings of the conference on Advances in neural information processing systems II, pp.368-374, 1999. ,
Feature Selection via concave minimization and support vector machines, Proceeding of ICML'98, pp.82-90, 1998. ,
Convex methods for transduction, Adv. in Neural Information Proc. Systems 16, p.73, 2004. ,
Zien A, Semi-supervised classification by low density separation, Proc. 10th Internat, pp.57-64, 2005. ,
Branch and bound for semi-supervised support vector machines, Advances in Neural Information Processing Systems, pp.217-224, 2006. ,
Keerthi Optimization Techniques for Semi-Supervised Support Vector Machines, Journal of Machine Learning Research, vol.9, pp.203-233, 2008. ,
A sparse large margin semi-supervised learning method, Journal of the Korean Statistical Society, vol.39, issue.4, p.479, 2010. ,
DOI : 10.1016/j.jkss.2009.10.005
Large scale transductive SVMs, J. Machine Learn, vol.7, pp.1687-1712, 2006. ,
An Approach for Incremental Semisupervised SVM, Seventh IEEE International Conference on Data Mining Workshops, pp.539-544, 2007. ,
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001. ,
DOI : 10.1198/016214501753382273
Semi-supervised support vector machines for unlabeled data classification, Optimization Methods and Software, p.29, 2001. ,
Sparse Quasi-Newton Optimization for Semi- Supervised Support Vector Machines, Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp.45-54, 2012. ,
Transductive inference for text classification using support vector machines, 16th Inter. Conf. on Machine Learning, pp.200-209, 1999. ,
Feature selection for support vector machines, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.712-715 ,
DOI : 10.1109/ICPR.2000.906174
The Adaptive Lasso and Its oracle Properties, Journal of the American Statistical Association, vol.101, pp.476-1418, 2006. ,
Leveraging the margin more carefully, Twenty-first international conference on Machine learning , ICML '04, pp.63-71, 2004. ,
DOI : 10.1145/1015330.1015344
Semi-supervised learning using label mean, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1-8, 2009. ,
DOI : 10.1145/1553374.1553456
ContributionàContribution`Contributionà l'optimisation non convexe et l'optimisation globale: Théorie, Algoritmes et Applications, HabilitationàHabilitation`Habilitationà Diriger des Recherches, 1997. ,
Solving a class of linearly constrained indefinite quadratic problems by DC algorithms, Journal of Global Optimization, vol.11, issue.3, pp.253-285, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01636781
The DC (difference of convex functions) Programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, vol.133, pp.23-46, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01636759
A new efficient algorithm based on DC programming and DCA for Clustering, Journal of Global Optimization, vol.37, pp.593-608, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-01636755
Optimization based DC programming and DCA for Hierarchical Clustering, European Journal of Operational Research, vol.183, pp.1067-1085, 2007. ,
A DC programming approach for feature selection in support vector machines learning, Advances in Data Analysis and Classification, vol.3, issue.1???3, pp.259-278, 2008. ,
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-01636751
Gene Selection for Cancer Classification Using DCA, Journal of Fonctiers of Computer Science and Technology, vol.3, issue.615, 2009. ,
DOI : 10.1007/978-3-540-88192-6_8
URL : https://hal.archives-ouvertes.fr/hal-01664633
A new approximation for the 0 -norm, 2012. ,
Feature selection in machine learning: an exact penalty approach using a Difference of Convex function Algorithm, Machine Learning, vol.36, issue.4, 2013. ,
DOI : 10.1214/009053607000000802
URL : https://hal.archives-ouvertes.fr/hal-01636662
Multicategory ??-Learning and Support Vector Machine: Computational Tools, Journal of Computational and Graphical Statistics, vol.14, issue.1, pp.219-236, 2005. ,
DOI : 10.1198/106186005X37238
A full smooth semi-support vector machine based on the cubic spline function, 2013 6th International Conference on Biomedical Engineering and Informatics, pp.650-655, 2013. ,
DOI : 10.1109/BMEI.2013.6747020
Combined SVM-Based Feature Selection and Classification, Machine Learning, pp.1-3129, 2005. ,
DOI : 10.1515/9781400873173
Text classification from labeled and unlabeled documents using EM, Machine Learning, pp.103-134, 2000. ,
Convex analysis approach to d.c. programming: Theory, Algorithm and Applications, Acta Mathematica Vietnamica, vol.22, pp.289-355, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01664714
Recent Advances in DC Programming and DCA, Transactions on Computational Intelligence XIII Lecture Notes in Computer Science, vol.8342, pp.1-37, 2014. ,
DOI : 10.1007/978-3-642-54455-2_1
URL : https://hal.archives-ouvertes.fr/hal-01664024
A bilinear formulation for vector sparsity optimization, Signal Processing, vol.88, issue.2, pp.375-389, 2008. ,
DOI : 10.1016/j.sigpro.2007.08.015
Learning sparse classifiers with Difference of Convex functions Algorithms, Optimization Methods and Software, p.4, 2013. ,
Cost-Sensitive Support Vector Machine for Semi-Supervised Learning, Procedia Computer Science, vol.18, pp.1684-1689, 2013. ,
DOI : 10.1016/j.procs.2013.05.336
DC programming approach for a class of nonconvex programs involving l 0 norm, in " Modelling, Computation and Optimization in Information Systems and Management Sciences, Communications in Computer and Information Science CCIS, vol.14, pp.358-367, 2008. ,
Variable Selection Using SVM-based Criteria, Journal of Machine Learning Research, vol.3, pp.1357-1370, 2003. ,
Trading Convexity for Scalability, Proceedings of the 23rd international conference on Machine learning ICML 2006, pp.201-208, 2006. ,
Simultaneous Feature Selection and Classification via Semi-Supervised Models, Proceeding ICNC '07, Third International Conference on Natural Computation-Cover, pp.646-650, 2007. ,
Deterministic annealing for semi-supervised kernel machines, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.841-848, 2006. ,
DOI : 10.1145/1143844.1143950
Large scale semi-supervised linear SVMs, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '06, pp.477-484, 2006. ,
DOI : 10.1145/1148170.1148253
Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc, vol.46, pp.431-439, 1996. ,
Le Thi A DC programming approach for Sparse Eigenvalue Problem, Proceeding of ICML 2010, pp.1063-1070, 2010. ,
On structural risk minimization or overall risk in a problem of pattern recognition . Automation and Remote Control, pp.1495-1498, 1977. ,
Semi-Supervised Learning for Classification with Uncertainty, Advanced Materials Research, vol.433, issue.440, pp.433-440, 2012. ,
DOI : 10.4028/www.scientific.net/AMR.433-440.3584
The Adaptive Lasso and Its Oracle Properties, Journal of the American Statistical Association, vol.101, issue.476, pp.1418-1429, 2006. ,
DOI : 10.1198/016214506000000735
Introduction to Semi-Supervised Learning, Synthesis Lectures on Artificial Intelligence and Machine Learning, vol.35, issue.8, pp.1598295470-9781598295474, 2009. ,
DOI : 10.1109/TKDE.2005.186
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso, Journal of Business & Economic Statistics, vol.25, issue.3, pp.347-355, 2007. ,
DOI : 10.1198/073500106000000251
Use of the Zero-Norm with Linear Models and Kernel Methods, Journal of Machine Learning Research, vol.3, pp.1439-1461, 2003. ,
Feature selection via concave minimization and support vector machines, Table 5 Comparative results of l References 1 Machine Learning Proceedings of the Fifteenth International Conferences (ICML'98), pp.82-90, 1998. ,
Multi-Class l 2,1 -Norm Support Vector Machine, Data Mining (ICDM), 2011 IEEE 11th International Conference, pp.91-100, 2011. ,
Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008. ,
DOI : 10.1007/978-1-4757-4182-7
Multi-Class Feature Selection with Support Vector Machines, 2008. ,
Multi-class feature selection for texture classification, Pattern Recognition Letters, vol.27, issue.14, pp.1685-1691, 2006. ,
DOI : 10.1016/j.patrec.2006.03.013
Combining SVMs with Various Feature Selection Strategies, Studies in Fuzziness and Soft Computing Volume, pp.315-347 ,
DOI : 10.1007/978-3-540-35488-8_13
Large scale transductive SVMs, J. Machine Learn, vol.7, pp.1687-1712, 2006. ,
A feature-selection algorithm based on Support Vector Machine-Multiclass for hyperspectral visible spectral analysis, Journal of Food Engineering, vol.119, issue.1, pp.159-166, 2013. ,
DOI : 10.1016/j.jfoodeng.2013.05.024
Multiple SVM-RFE for Gene Selection in Cancer Classification With Expression Data, IEEE Transactions on Nanobioscience, vol.4, issue.3, pp.228-234, 2005. ,
DOI : 10.1109/TNB.2005.853657
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001. ,
DOI : 10.1198/016214501753382273
An Introduction to Variable and Feature Selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
Feature selection for support vector machines, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.712-715, 2000. ,
DOI : 10.1109/ICPR.2000.906174
A comparison of methods for multiclass support vector machines, IEEE Transactions on Neural Networks, vol.13, issue.2, pp.415-425, 2002. ,
The Adaptive Lasso and Its oracle Properties, Journal of the American Statistical Association, vol.101, pp.476-1418, 2006. ,
Adaptive Lasso for sparse high-dimentional regression models, Statistica Sinica, vol.18, pp.1603-1618, 2008. ,
Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification, Cancer Informatics, vol.12, pp.143-153, 2013. ,
DOI : 10.4137/CIN.S10212
DC Programming and DCA ,
URL : https://hal.archives-ouvertes.fr/hal-01664024
A new approximation for the 0 -norm, 2012. ,
The DC (Difference of convex functions) programming and DCA revisited with DC models of real world nonconvex optimization problems, Annals of Operations Research, vol.133, pp.23-46, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01636759
A new efficient algorithm based on DC programming and DCA for Clustering, Journal of Global Optimization, vol.37, pp.593-608, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-01636755
Optimization based DC programming and DCA for Hierarchical Clustering, European Journal of Operational Research, vol.183, pp.1067-1085, 2007. ,
A DC programming approach for feature selection in support vector machines learning, Advances in Data Analysis and Classification, vol.3, issue.1???3, pp.259-278, 2008. ,
DOI : 10.1007/978-1-4757-2440-0
URL : https://hal.archives-ouvertes.fr/hal-01636751
Gene Selection for Cancer Classification Using DCA, ADMA 2008, pp.62-72 ,
URL : https://hal.archives-ouvertes.fr/hal-01664633
Exact Penalty and Error Bounds in DC Programming Journal of Global Optimization dedicated to Reiner Horst, 2011. ,
Structured multicategory support vector machines with analysis of variance decomposition, Biometrika, vol.93, issue.3, pp.555-71, 2006. ,
DOI : 10.1093/biomet/93.3.555
Multicategory support vector machines, theory, and application to the classification of microarray data and satellite radiance data, Journal of the American Statistical Association, vol.99, pp.465-67, 2004. ,
Feature Selection for Multi-class Problems Using Support Vector Machines, PRICAI Trends in Artificial Intelligence Lecture Notes in Computer Science, vol.3157, pp.292-300, 2004. ,
DOI : 10.1007/978-3-540-28633-2_32
Multicategory ? -Learning, Journal of the American Statistical Association, vol.101, pp.474-500, 2006. ,
URL : https://hal.archives-ouvertes.fr/hal-01020707
Support vector machines with adaptive penalty, Computational Statistics & Data Analysis, vol.51, issue.12, pp.6380-6394, 2007. ,
DOI : 10.1016/j.csda.2007.02.006
An iterative SVM approach to feature selection and classification in high-dimensional datasets, Pattern Recognition, vol.46, issue.9, pp.2531-2537, 2013. ,
DOI : 10.1016/j.patcog.2013.02.007
Simultaneous feature selection and classification using kernel-penalized support vector machines, Information Sciences, vol.181, issue.1, pp.115-128, 2011. ,
DOI : 10.1016/j.ins.2010.08.047
SVM-Based Feature Selection by Direct Objective Minimisation, Proc. of 26th DAGM Symposium Pattern Recognition, pp.212-219, 2004. ,
DOI : 10.1007/978-3-540-28649-3_26
Learning sparse classifiers with difference of convex functions algorithms, Optimization Methods and Software, vol.37, issue.5, p.4, 2013. ,
DOI : 10.1145/1553374.1553536
URL : https://hal.archives-ouvertes.fr/hal-01636678
A bilinear formulation for vector sparsity optimization, Signal Processing, vol.88, issue.2, pp.375-389, 2008. ,
DOI : 10.1016/j.sigpro.2007.08.015
Convex analysis approach to d.c. programming: Theory, Algorithm and Applications, Acta Mathematica Vietnamica, vol.22, pp.289-355, 1997. ,
URL : https://hal.archives-ouvertes.fr/hal-01664714
Optimization algorithms for solving the trust region subproblem, SIAMJ. Optimization, vol.2, pp.476-505, 1998. ,
Recent Advances in DC Programming and DCA, Transactions on Computational Intelligence XIII Lecture Notes in Computer Science, vol.8342, pp.1-37, 2014. ,
DOI : 10.1007/978-3-642-54455-2_1
URL : https://hal.archives-ouvertes.fr/hal-01664024
Variable Selection Using SVM-based Criteria, Journal of Machine Learning Research, vol.3, pp.1357-1370, 2003. ,
Multiclass Feature Selection With Kernel Gram-Matrix-Based Criteria, IEEE Transactions on Neural Networks and Learning Systems, vol.23, issue.10, pp.10-1611, 2012. ,
DOI : 10.1109/TNNLS.2012.2201748
Trading Convexity for Scalability, Proceedings of the 23rd international conference on Machine learning ICML 2006, pp.201-208, 2006. ,
Group lasso regularized multiple kernel learning for heterogeneous feature selection, The 2011 International Joint Conference on Neural Networks, pp.2570-2577, 2011. ,
DOI : 10.1109/IJCNN.2011.6033554
Robust Regression Shrinkage and Consistent Variable Selection Through the LAD-Lasso, Journal of Business & Economic Statistics, vol.25, issue.3, pp.347-355, 2007. ,
DOI : 10.1198/073500106000000251
On L_1-Norm Multi-class Support Vector Machines, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06), pp.583-594, 2003. ,
DOI : 10.1109/ICMLA.2006.38
Support Vector Machines for Multi-Class Pattern Recognition, Proceedings - European Symposium on Artificial Neural Networks, ESANN 1999, pp.219-224, 1999. ,
Use of Zero-Norm with Linear Models and Kernel Methods, Journal of Machine Learning Research, vol.3, pp.1439-1461, 2003. ,
A Probabilistic Approach to Feature Selection for Multi-class Text Categorization, Part I, LNCS 4491, pp.1310-1317, 2007. ,
DOI : 10.1016/S0031-3203(03)00062-1
Variable selection for the multicategory SVM via adaptive sup-norm regularization, Electronic Journal of Statistics, vol.2, issue.0, pp.149-167, 2008. ,
DOI : 10.1214/08-EJS122
MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data, Bioinformatics, vol.22, issue.9, pp.1106-1114, 2007. ,
DOI : 10.1093/bioinformatics/btl438
Exclusive Lasso for Multi-task Feature Selection, 2010. ,